Inflation and Hyperinflation Countries in 2018–2020: Risks of Different Assets and Foreign Trade
Bibliographic record
Abstract
Since the global financial crisis (2008–2009), central banks and governments in developed countries have relied upon loose monetary and financial policy. In the coronavirus pandemic era, these policies were taken even more to the extreme. In 2021, countries around the world started to experience product availability issues, and inflation in some cases was extremely high. There has been debate about the possibility of persistent high inflation. However, risks to assets and foreign trade in this new situation are unknown as all important hyperinflation cases are from decades to century-old. It is important to know what kind of implications high inflation has on modern economies. Therefore, in this study, 10 countries with the highest inflation were selected to be examined in the period of 2018–2020. In these countries, currencies lost a considerable amount of their value against US dollar in 2018–2020. Stock market indexes in many cases provided very high returns in local currency terms; however, against the US dollar, the index yield changed for the substantially negative. Apartment prices in general declined as well. In foreign trade, imports generally declined, while exports were mixed or even increased. However, it should be noted that all of these observations are influenced by the pandemic era and special circumstances of a particular country.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".